[{"data":1,"prerenderedAt":811},["ShallowReactive",2],{"/en-us/blog/getting-started-with-gitlab-understanding-ci-cd":3,"navigation-en-us":43,"banner-en-us":452,"footer-en-us":462,"blog-post-authors-en-us-GitLab":702,"blog-related-posts-en-us-getting-started-with-gitlab-understanding-ci-cd":716,"blog-promotions-en-us":748,"next-steps-en-us":801},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":28,"isFeatured":12,"meta":29,"navigation":12,"path":30,"publishedDate":20,"seo":31,"stem":36,"tagSlugs":37,"__hash__":42},"blogPosts/en-us/blog/getting-started-with-gitlab-understanding-ci-cd.yml","Getting Started With Gitlab Understanding Ci Cd",[7],"gitlab",null,"product",{"slug":11,"featured":12,"template":13},"getting-started-with-gitlab-understanding-ci-cd",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Getting started with GitLab: Understanding CI/CD","Learn the basics of continuous integration/continuous delivery in this beginner's guide, including what CI/CD components are and how to create them.",[18],"GitLab","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659525/Blog/Hero%20Images/blog-getting-started-with-gitlab-banner-0497-option4-fy25.png","2025-04-25","*Welcome to our \"Getting started with GitLab\" series, where we help newcomers get familiar with the GitLab DevSecOps platform.*\n\nImagine a workflow where every code change is automatically built, tested, and deployed to your users. That's the power of [Continuous Integration/Continuous Delivery (CI/CD)](https://about.gitlab.com/topics/ci-cd/)! CI/CD helps you catch bugs early, ensures code quality, and delivers software faster and more frequently.\n\n### What is CI/CD?\n\n* **Continuous Integration** is a development practice where developers integrate code changes into a shared repository frequently, preferably several times a day. Each integration is then verified by an automated build and test process, allowing teams to detect problems early.  \n* **Continuous Delivery** extends CI by automating the release pipeline, ensuring that your code is *always* in a deployable state. You can deploy your application to various environments (e.g., staging, production) with a single click or automatically.  \n* **Continuous Deployment** takes it a step further by automatically deploying *every successful build* to production. This requires a high degree of confidence in your automated tests and deployment process.\n\n### Why GitLab CI/CD?\n\nGitLab CI/CD is a powerful, integrated system that comes built-in with GitLab. It offers a seamless experience for automating your entire software development lifecycle. With GitLab CI/CD, you can:\n\n* **Automate everything:** Build, test, and deploy your applications with ease.  \n* **Catch bugs early:** Detect and fix errors before they reach production.  \n* **Get faster feedback:** Receive immediate feedback on your code changes.  \n* **Improve collaboration:** Work together more effectively with automated workflows.  \n* **Accelerate delivery:** Release software faster and more frequently.  \n* **Reduce risk:** Minimize deployment errors and rollbacks.\n\n### The elements of GitLab CI/CD\n\n* `.gitlab-ci.yml`**:** This [YAML file](https://docs.gitlab.com/ee/ci/yaml/), located in your project's root directory, defines your CI/CD pipeline, including stages, jobs, and runners.  \n* [**GitLab Runner**](https://docs.gitlab.com/runner/)**:** This agent executes your CI/CD jobs on your infrastructure (e.g. physical machines, virtual machines, Docker containers, or Kubernetes clusters).  \n* [**Stages**](https://docs.gitlab.com/ee/ci/yaml/#stages)**:** Stages define the order of execution for your jobs (e.g. build, test, and deploy).  \n* [**Jobs**](https://docs.gitlab.com/ee/ci/yaml/#job-keywords)**:** Jobs are individual units of work within a stage (e.g. compile code, run tests, and deploy to staging).\n\n### Setting up GitLab CI\n\nGetting started with GitLab CI is simple. Here's a basic example of a `.gitlab-ci.yml` file:\n\n```yaml\nstages:\n  - build\n  - test\n  - deploy\n\nbuild_job:\n  stage: build\n  script:\n    - echo \"Building the application...\"\n\ntest_job:\n  stage: test\n  script:\n    - echo \"Running tests...\"\n\ndeploy_job:\n  stage: deploy\n  script:\n    - echo \"Deploying to production...\"\n  environment:\n    name: production\n\n```\n\nThis configuration defines three stages: \"build,\" \"test,\" and \"deploy.\" Each stage contains a job that executes a simple script.\n\n### CI/CD configuration examples\n\nLet's explore some more realistic examples.\n\n**Building and deploying a Node.js application**\n\nThe pipeline definition below outlines using npm to build and test a Node.js application and [dpl](https://docs.gitlab.com/ci/examples/deployment/) to deploy the application to Heroku. The deploy stage of the pipeline makes use of [GitLab CI/CD variables](https://docs.gitlab.com/ci/variables/), which allow developers to store sensitive information (e.g. credentials) and securely use them in CI/CD processes. In this example, an API key to deploy to Heroku is stored under the variable key name `$HEROKU_API_KEY` used by the dpl tool.\n\n```yaml\nstages:\n  - build\n  - test\n  - deploy\n\nbuild:\n  stage: build\n  image: node:latest\n  script:\n    - npm install\n    - npm run build\n\ntest:\n  stage: test\n  image: node:latest\n  script:\n    - npm run test\n\ndeploy:\n  stage: deploy\n  image: ruby:latest\n  script:\n    - gem install dpl\n    - dpl --provider=heroku --app=$HEROKU_APP_NAME --api-key=$HEROKU_API_KEY\n\n```\n\n**Deploying to different environments (staging and production)**\n\nGitLab also offers the idea of [Environments](https://docs.gitlab.com/ci/environments/) with CI/CD. This feature allows users to track deployments from CI/CD to infrastructure targets. In the example below, the pipeline adds stages with an environment property for a staging and production environment. While the deploy_staging stage will always run its script, the deploy_production stage requires manual approval to prevent accidental deployment to production.  \n\n```yaml\nstages:\n  - build\n  - test\n  - deploy_staging\n  - deploy_production\n\nbuild:\n  # ...\n\ntest:\n  # ...\n\ndeploy_staging:\n  stage: deploy_staging\n  script:\n    - echo \"Deploying to staging...\"\n  environment:\n    name: staging\n\ndeploy_production:\n  stage: deploy_production\n  script:\n    - echo \"Deploying to production...\"\n  environment:\n    name: production\n  when: manual  # Requires manual approval\n\n```\n\n### GitLab Auto DevOps\n\n[GitLab Auto DevOps](https://docs.gitlab.com/ee/topics/autodevops/) simplifies CI/CD by providing a pre-defined configuration that automatically builds, tests, and deploys your applications. It leverages best practices and industry standards to streamline your workflow.\n\nTo enable Auto DevOps:\n\n1. Go to your project's **Settings > CI/CD > General pipelines**.  \n2. Enable the **Auto DevOps** option.\n\nAuto DevOps automatically detects your project's language and framework and configures the necessary build, test, and deployment stages. You don’t even need to create a `.gitlab-ci.yml` file.\n\n### CI/CD Catalog\n\nThe [CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/) is a list of projects with published [CI/CD components](https://docs.gitlab.com/ee/ci/components/) you can use to extend your CI/CD workflow. Anyone can create a component project and add it to the CI/CD Catalog or contribute to an existing project to improve the available components. You can find published components in the [CI/CD Catalog](https://gitlab.com/explore/catalog) on GitLab.com.\n\n> [Tutorial: How to set up your first GitLab CI/CD component](https://about.gitlab.com/blog/tutorial-how-to-set-up-your-first-gitlab-ci-cd-component/)\n\n### CI templates\n\nYou can also create your own [CI templates](https://docs.gitlab.com/ee/ci/examples/) to standardize and reuse CI/CD configurations across multiple projects. This promotes consistency and reduces duplication.\n\nTo create a CI template:\n\n1. Create a `.gitlab-ci.yml` file in a dedicated project or repository.  \n2. Define your CI/CD configuration in the template.  \n3. In your project's `.gitlab-ci.yml` file, use the `include` keyword to include the template.\n\n## Take your development to the next level\n\nGitLab CI/CD is a powerful tool that can transform your development workflow. By understanding the concepts of CI/CD, configuring your pipelines, and leveraging features like Auto DevOps, the CI/CD Catalog, and CI templates, you can automate your entire software development lifecycle and deliver high-quality software faster and more efficiently.\n\n> Want to take your learning to the next level? Sign up for [GitLab University courses](https://university.gitlab.com/). Or you can get going right away with a [free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/).\n\n## \"Getting Started with GitLab\" series\n\nCheck out more articles in our \"Getting Started with GitLab\" series:\n\n- [How to manage users](https://about.gitlab.com/blog/getting-started-with-gitlab-how-to-manage-users/)\n- [How to import your projects to GitLab](https://about.gitlab.com/blog/getting-started-with-gitlab-how-to-import-your-projects-to-gitlab/)  \n- [Mastering project management](https://about.gitlab.com/blog/getting-started-with-gitlab-mastering-project-management/)\n- [Automating Agile workflows with the gitlab-triage gem](https://about.gitlab.com/blog/automating-agile-workflows-with-the-gitlab-triage-gem/)\n- [Working with CI/CD variables](https://about.gitlab.com/blog/getting-started-with-gitlab-working-with-ci-cd-variables/)\n",[23,24,25,26,9,27],"CI/CD","CI","CD","DevSecOps 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18.11: Budget guardrails for GitLab Credits","Learn how new spending caps and per-user credit limits give organizations the budget guardrails to scale GitLab Duo Agent Platform.",[722],"Bryan Rothwell","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776259080/cakqnwo5ecp255lo8lzo.png","2026-04-16","Teams using GitLab Duo Agent Platform with on-demand GitLab Credits are shipping faster, catching bugs earlier, and automating tasks that used to take entire sprints. But as adoption grows, so does oversight from finance, procurement, and platform teams to prove that AI spending is bounded, predictable, and controllable.\n\nOne of the greatest barriers to broader AI adoption isn't skepticism about the technology. It's uncertainty about managing spend. Without budget caps, a busy month could produce unexpected expenses. Without per-user limits, a handful of power users could burn through the team's credits before the month is over. And without either, engineering leaders who want to expand their use of agentic AI for software development have to jump through more hoops for budget approval.\n\nSince its [general availability](https://about.gitlab.com/blog/gitlab-duo-agent-platform-is-generally-available/), GitLab Duo Agent Platform has provided usage governance and visibility. With GitLab 18.11, we're introducing usage controls for [GitLab Credits](https://about.gitlab.com/blog/introducing-gitlab-credits/): spending caps and budget guardrails that give your organization even more control and transparency over how credits are consumed.\n\n## Managing GitLab Credits\n\nGitLab 18.11 adds three layers of control over GitLab Credits consumption: a subscription-level spending cap, per-user credit limits, and visibility into cap status and enforcement.\n\n### Subscription-level spending cap\n\nBilling account managers can now set a hard monthly ceiling for on-demand GitLab Credits consumption for their entire subscription.\n\nHere's how it works:\n\n* **Set a cap** in the `Customers Portal` under your subscription's GitLab Credits settings.  \n* **Enforce spend limits automatically.**  When on-demand usage reaches the cap, DAP access is paused for all users on that subscription until the next monthly period begins.  \n* **Make adjustments as you go.** Raise or disable the cap mid-month to restore access.\n\nThe cap resets each monthly period and your configured limit carries forward unless you change it. Because usage data is synchronized periodically rather than in real time, a small amount of additional usage may occur after the cap is reached before enforcement takes effect. See the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for details.\n\n### User-level spending caps\n\nNot every user consumes credits at the same rate, and that's expected. But when one or two power users account for a disproportionate share of the pool, the rest of the team can lose access before the month is over.\n\nPer-user credit caps prevent any single user from consuming more than their fair share:\n\n* **Flat per-user cap.** Set a uniform credit limit that applies equally to every user on the subscription through the GitLab GraphQL API. Unlike the subscription-level cap, the per-user cap applies to a user's total consumption across all credit sources.  \n* **Custom per-user overrides.** For organizations that need differentiated limits, you can set individual credit caps for specific users through the GraphQL API. For example, you could give your staff engineers a higher allocation while applying a standard limit to the broader team.  \n* **Individual enforcement.** When a user reaches their cap, they retain full access to GitLab. Only their Duo Agent Platform credit usage is paused until the next billing cycle. Everyone else keeps working uninterrupted until they hit their own limit or the subscription-level cap is reached, whichever comes first.\n\n### Visibility and notifications\n\nWhen a subscription-level cap is reached, GitLab sends an email notification to billing account managers so they can take action: raise the cap, wait for the next period, or redistribute credits.\n\nWithin GitLab, group owners (GitLab.com) and instance administrators (Self-Managed) can view which users have been blocked due to reaching their per-user cap and restore access by adjusting the cap through the GraphQL API. \n\n## How budget guardrails help organizations scale AI usage\n\nGuardrails are essential as organizations ramp up their AI adoption. Here's why:\n\n### Predictable AI budgets\n\nUsage controls for GitLab Duo Agent Platform turn AI into a bounded, predictable budget item using on-demand GitLab Credits. That makes it easier to deploy agents across the software development lifecycle and get sign-off from finance, justify renewals, and plan quarterly spend.\n\n### Governance and chargeback\n\nLarge organizations often need to align AI consumption with internal budgets, cost centers, or departmental policies. Per-user caps give platform teams a straightforward mechanism to allocate credits fairly and track consumption at the individual level. The API import options make it practical to manage caps at enterprise scale. Combined with per-user usage data from the GitLab Credits dashboard, organizations can track consumption patterns to inform their own internal chargeback or budget allocation processes.\n\n### Confidence to scale\n\nMany customers start GitLab Duo Agent Platform with a small pilot group. Usage controls remove risks associated with expanding that pilot across the organization. You can roll out Duo Agent Platform to hundreds or thousands of developers knowing there's a hard ceiling protecting your budget. If usage grows faster than expected, you'll hit the cap, not an unexpected invoice.\n\n## Addressing the seat-based and visibility conundrum\n\nMany AI coding tools take a seat-based approach to cost management. You buy a fixed number of seats at a flat per-user price, and that's your budget. It's simple, but rigid. You pay the same whether a developer uses the tool ten times a day or never touches it. And as vendors introduce premium models and usage-based overages on top of seat pricing, the cost predictability that seat-based licensing promised starts to erode.\n\n\nGitLab takes a different approach. Usage-based pricing with hard caps and a single governance dashboard. You get the flexibility of paying for what your teams actually use, with the budget predictability of enforced spending limits.\n\n## Real-world usage controls\n\n**One example is a mid-size SaaS customer that wants to protect their monthly budget.** A 200-person engineering organization sets a subscription-level cap equal to their expected on-demand usage. Their VP of Engineering can confidently tell finance that GitLab Duo Agent Platform spend will never exceed the approved amount, even as they onboard new teams. If they approach the cap mid-month, the billing account manager gets a notification and can decide whether to raise the limit or wait for the next period.\n\n**At GitLab, we also work with large enterprises that want to keep usage fair across teams.** A global financial services company with 2,000 developers uses per-user caps to ensure equitable access. Staff engineers working on complex refactoring projects get a higher individual allocation via API, while most developers receive a standard flat cap. No single user can exhaust the pool, and the platform team uses the per-user usage data in the GitLab Credits dashboard to track consumption patterns and inform quarterly budget planning.\n\n## Getting started\n\nUsage controls are available for both GitLab.com and Self-Managed customers running GitLab 18.11. Different controls are configured in different places depending on the scope and your role.\n\n**Subscription-level cap**\n\nBilling account managers set the subscription-level on-demand cap in the Customers Portal:\n\n1. Sign in to the `Customers Portal`.  \n2. On your subscription card, navigate to **GitLab Credits** settings.  \n3. Enable the monthly on-demand credits cap and enter your desired limit.\n\n**Flat per-user cap**\n\nThe flat per-user cap can be set through the GitLab GraphQL API by namespace owners (GitLab.com) or instance administrators (Self-Managed). Check the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for the latest on available configuration surfaces.\n\n**Custom per-user overrides**\n\nFor differentiated limits, namespace owners (GitLab.com) and instance administrators (Self-Managed) can set individual caps programmatically. This is useful for automation and infrastructure-as-code workflows.\n\n**Monitor usage and cap status**\n\n* **Customers Portal:** View detailed usage and cap status.  \n* **GitLab.com:** Group owners can view blocked users under **Settings > GitLab Credits**.  \n* **Self-Managed:** Instance administrators can view cap status and blocked users under **Admin > GitLab Credits**.\n\n## GitLab Duo Agent Platform is ready to scale\n\nUsage controls are available now in GitLab 18.11. If you've been waiting for the right guardrails before expanding GitLab Duo Agent Platform across your organization, this is your moment. Set your caps, roll out Duo Agent Platform to more teams, and start shipping faster!\n\n> [Learn more about GitLab Credits and usage controls](https://docs.gitlab.com/subscriptions/gitlab_credits/).",[9,727,728],"AI/ML","news",{"featured":32,"template":13,"slug":730},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":732,"config":735},{"title":733,"heroImage":723,"description":734,"date":724,"category":9},"GitLab 18.11 release","This release includes Agentic SAST Vulnerability Resolution, Data Analyst Foundational Agent, CI Expert Agent, and more.",{"featured":32,"template":13,"externalUrl":736},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":738,"config":746},{"title":739,"description":740,"authors":741,"heroImage":723,"date":724,"body":743,"category":9,"tags":744},"GitLab 18.11: CI Expert and Data Analyst AI agents target development gaps","Set up CI and query your software development lifecycle data with two new GitLab Duo Agent Platform foundational agents available in GitLab 18.11.",[742],"Corinne Dent","AI-generated code moves faster than the systems around it can keep up with. More code means more merge requests queued, more pipelines to configure, more questions about delivery that nobody has time to answer — and most of the tooling teams rely on wasn't built for this pace.\n\nIn GitLab 18.11, two new foundational agents for Duo Agent Platform address specific gaps in the development lifecycle that AI has largely left untouched:\n* CI Expert Agent (now in beta) focuses on the gap between writing code and getting it into a running pipeline\n* Data Analyst Agent (now generally available) focuses on the gap between shipping code and being able to answer basic questions about how that delivery is actually going.\n\n\nThese are problem areas that couldn't be solved by a general-purpose assistant. A tool running outside GitLab can generate a YAML file or answer a question, but it has no awareness of how your pipelines have historically performed, where failures cluster, or what your actual MR cycle times look like. That context lives in GitLab. These agents do too.\n## Fast CI setup with CI Expert Agent\n\nAI has made it easier than ever to write code. Getting that code into a running pipeline is still something most teams do days, or weeks, later — if at all. The blank-page problem isn't in the editor anymore. The blank page is now in `.gitlab-ci.yml`.\n\nDevelopers who have never configured CI don't know what language detection looks like in YAML, what their test commands should be, or how to validate the result before pushing. Teams either copy a config from a previous project that may not fit, stitch together examples from documentation, or wait for the one person who's done it before. If that person isn't available, CI becomes the thing you'll \"get to later.\" Later becomes never.\n\nWhen CI never happens, the impact shows up everywhere else. Changes ship without a reliable safety net, regressions surface in production instead of in pipelines, and work piles up in bigger, riskier batches because no one wants to be the person who “breaks the build.” Over time, teams normalize working in the dark, often relying on undocumented institutional knowledge and ad-hoc testing, instead of having a fast, predictable feedback loop baked into every change.\n\nCI Expert Agent, now available in beta, removes that friction. It inspects your repository, identifies your language and framework, and proposes a working build and test pipeline tailored to what's actually there — then explains every decision in plain language. The target: a running pipeline in minutes, with no YAML written by hand.\n\nWhat CI Expert Agent does:\n\n* Repo-aware pipeline generation detects language, framework, and test setup \n* Generates valid, runnable build and test configurations   \n* Guided first-pipeline flow with plain-language explanation of each step in Agentic Chat  \n* Native GitLab CI semantics with no config translation required\n\nBecause it runs inside GitLab and sees real pipeline behavior over time, each improvement can build on how teams actually work, not just on static examples.\n\u003Ciframe src=\"https://player.vimeo.com/video/1183458036?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"CI/CD Expert Agent\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cbr>\u003C/br>\n\nCI Expert Agent is available on GitLab.com, Self-Managed, Dedicated; Free, Premium, Ultimate Editions with Duo Agent Platform enabled.\n\n## Query GitLab data in plain language with Data Analyst Agent\n\nAI has sped up how teams ship. Answering basic questions about how that work is going has gotten harder, not easier.\n\nHow long are MRs sitting in review? Which pipelines are slowing teams down? Are deployment targets actually being hit? These questions used to be answerable by glancing at a dashboard. Now, with more code, more teams, and more complexity, the data exists — it's in GitLab — but accessing it still means waiting on an analytics team, filing a dashboard request, or learning GLQL.\n\nData Analyst Agent targets that gap. Ask a natural-language question and get an instant visualization in Agentic Chat. No query language, no dashboard request, no waiting for the answers to be assembled by someone else.\n\nFor example, the agent can answer questions about the following topics for these roles:\n\n* Engineering managers: MR cycle time, throughput by project, where reviews get stuck  \n* Developers: Contribution patterns, flaky tests blocking their MRs, pipeline speed trends  \n* DevOps and platform engineers: Pipeline success/failure rates, runner utilization, deployment frequency  \n* Engineering leadership: Cross-portfolio deployment frequency, project health metrics, lead time comparisons\n\nNow generally available in 18.11, the agent covers MRs, issues, projects, pipelines, and jobs — full software development lifecycle coverage, expanded from the beta scope. Because Data Analyst Agent queries what's already in GitLab, the context is always current, and there's no pipeline to maintain or third-party tool to keep synchronized. Generated GitLab Query Language queries can be copied and used anywhere GitLab Flavored Markdown is supported, with direct export to work items and dashboards on the roadmap.\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1183094817?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Data Analyst agent demo\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cbr>\u003C/br>\n\nData Analyst Agent is available on GitLab.com, Self-Managed, Dedicated; Free, Premium and Ultimate Edition with Duo Agent Platform enabled.\n\n## One platform, connected context\n\nBoth agents run inside GitLab, with access to the code, pipelines, issues, and merge requests already there. That's what separates platform-native AI from a disconnected assistant: the context is always current, and it only gets more useful over time. CI Expert Agent and Data Analyst Agent represent two concrete steps toward a platform where AI doesn't just help you write code faster; it helps you understand, ship, and maintain what gets built.\n\n> [Start a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo/) to experience these foundational AI agents.",[727,745,9],"features",{"featured":12,"template":13,"slug":747},"ci-expert-and-data-analyst-ai-agents-target-development-gaps",{"promotions":749},[750,764,775,787],{"id":751,"categories":752,"header":754,"text":755,"button":756,"image":761},"ai-modernization",[753],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":757,"config":758},"Get your AI maturity score",{"href":759,"dataGaName":760,"dataGaLocation":246},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":762},{"src":763},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":765,"categories":766,"header":767,"text":755,"button":768,"image":772},"devops-modernization",[9,570],"Are you just managing tools or shipping innovation?",{"text":769,"config":770},"Get your DevOps maturity score",{"href":771,"dataGaName":760,"dataGaLocation":246},"/assessments/devops-modernization-assessment/",{"config":773},{"src":774},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":776,"categories":777,"header":779,"text":755,"button":780,"image":784},"security-modernization",[778],"security","Are you trading speed for security?",{"text":781,"config":782},"Get your security maturity score",{"href":783,"dataGaName":760,"dataGaLocation":246},"/assessments/security-modernization-assessment/",{"config":785},{"src":786},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":788,"paths":789,"header":792,"text":793,"button":794,"image":799},"github-azure-migration",[790,791],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":795,"config":796},"See how GitLab compares to GitHub",{"href":797,"dataGaName":798,"dataGaLocation":246},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":800},{"src":774},{"header":802,"blurb":803,"button":804,"secondaryButton":809},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":805,"config":806},"Get your free trial",{"href":807,"dataGaName":54,"dataGaLocation":808},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":508,"config":810},{"href":58,"dataGaName":59,"dataGaLocation":808},1776443042678]